Mine safety monitoring and early warning system based on target detection

To further strengthen and improve the coal mine safety monitoring system, a coal mine safety monitoring and early warning system was designed and developed based on deep learning object detection technology. The YOLOv5 network model serves as the core algorithm of the system, and by incorporating the CBAM attention mechanism, it addresses the issue of potential information loss during object detection in complex mine environments, achieving precise recognition and classification of underground video images. A hierarchical system architecture was established by building an industrial ring network and deploying cameras and sensors to acquire and transmit real-time monitoring data. This enables the analysis and identification of unsafe behaviors of personnel in video images, while simultaneously monitoring and providing early warnings for the underground environment and the operational status of production equipment. The system is well-suited to adapt to the complex underground environment, promptly identifying unsafe factors related to personnel, equipment, and environmental conditions during the production process. It issues early warning signals to facilitate timely and efficient response measures, providing strong technical support for the intelligent construction of coal mine safety monitoring.